4 ms·

I believe the censorship is at the semantic level, not the token level. Same way RL allows training model responses independent of eventual input/output languag

I believe the censorship is at the semantic level, not the token level. Same way RL allows training model responses independent of eventual input/output languages. I’m sure the goal is to remove stuff in pre training, but it is sufficient to RL it away. Same way OpenAI models doubtlessly have training data relating to bio weapons or pedophilia, but it is pretty effectively suppressed via RL.

hntemp787609084 2y ago
Only played with DeepSeek-R1-Distill-Qwen-14B, but the knowledge is definitely still there. https://pastebin.com/H2UTdi78 https://pastebin.com/H2UTdi78 Seems more than happy to talk about Tienanmen, Xi, etc. starting at line 170 with the very primitive method of wrapping the query in its own "<think>...</think>" syntax even though it's the user role. Uyghurs are more strictly forbidden as a topic, as are its actual system prompts. None of this is serious jailbreaking, it was just interesting to see where and when it drew lines and that it switched to simplified Chinese at the end of the last scenario.
- geewee 2y ago
  That was an incredibly interesting read, thank you for sharing!
- deleted 2y ago
  [deleted]
- isoprophlex 2y ago
  That was intense, well done!
- joshstrange 2y ago
  Incredibly fascinating to read through. I don’t follow jailbreaking closely so maybe the tricks you used are well-known (I’ve seen 1-2 of them before I think) but I really enjoyed seeing how you tricked it. The user-written “<think>” blocks were genius as was stopping execution part way so you could inject stuff the LLM “thought” it said.
godelski 2y ago
> I believe the censorship is at the semantic level, not the token level. I'm sorry, what did I say that you're disagreeing to? Censorship can happen at various levels. Often at semantic, being that you'd censor this through the chat training procedures. There's also of course traditional filtering mechanisms which act as a backup for the model, which we see in this case. That's why it generates the string and then suddenly removes everything. There can be token censorship too, in that you can just not encode certain tokens or you can tune certain tokens to always provide a certain output. There are glitch tokens after all... And of course, there is latent censorship that you can do as well. Think about Golden Gate Claude, where they up-weight features. They do this for interpretability, but of course that can be used for censorship as well. What I'm saying is, there's many ways to skin a cat. In practice, more than one technique is used to complement one another. Probably would be silly to entirely rely on one thing and not have failsafes. What kind of "security" would that be?

hntemp787609084 2y ago

Only played with DeepSeek-R1-Distill-Qwen-14B, but the knowledge is definitely still there. https://pastebin.com/H2UTdi78 https://pastebin.com/H2UTdi78 Seems more than happy to talk about Tienanmen, Xi, etc. starting at line 170 with the very primitive method of wrapping the query in its own "<think>...</think>" syntax even though it's the user role. Uyghurs are more strictly forbidden as a topic, as are its actual system prompts. None of this is serious jailbreaking, it was just interesting to see where and when it drew lines and that it switched to simplified Chinese at the end of the last scenario.

geewee 2y ago
That was an incredibly interesting read, thank you for sharing!
deleted 2y ago
[deleted]
isoprophlex 2y ago
That was intense, well done!
joshstrange 2y ago
Incredibly fascinating to read through. I don’t follow jailbreaking closely so maybe the tricks you used are well-known (I’ve seen 1-2 of them before I think) but I really enjoyed seeing how you tricked it. The user-written “<think>” blocks were genius as was stopping execution part way so you could inject stuff the LLM “thought” it said.

godelski 2y ago

> I believe the censorship is at the semantic level, not the token level. I'm sorry, what did I say that you're disagreeing to? Censorship can happen at various levels. Often at semantic, being that you'd censor this through the chat training procedures. There's also of course traditional filtering mechanisms which act as a backup for the model, which we see in this case. That's why it generates the string and then suddenly removes everything. There can be token censorship too, in that you can just not encode certain tokens or you can tune certain tokens to always provide a certain output. There are glitch tokens after all... And of course, there is latent censorship that you can do as well. Think about Golden Gate Claude, where they up-weight features. They do this for interpretability, but of course that can be used for censorship as well. What I'm saying is, there's many ways to skin a cat. In practice, more than one technique is used to complement one another. Probably would be silly to entirely rely on one thing and not have failsafes. What kind of "security" would that be?